{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 大纲\n",
    "\n",
    "- 什么是机器学习\n",
    "- 机器学习有什么用\n",
    "- 机器学习分类\n",
    "- 机器学习应用开发典型步骤\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.机器学习分类\n",
    "\n",
    "---\n",
    "机器学习可以分成两类:\n",
    "- 有监督学习[supervised learing]: 通过大量已知的输入和输出相匹配的数据,让计算机从中学习规律,从而能针对一个新的输入做出合理的输出预测.\n",
    "    - 回归学习\n",
    "    - 分类学习\n",
    "- 无监督学习[unsupervised learning]: 通过学习大量无标记的数据,去分析数据本身的内在结构和特点\n",
    "    - 聚类问题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.机器学习应用开发的典型步骤\n",
    "\n",
    "1. 数据采集和标记\n",
    "2. 数据清洗\n",
    "3. 特征选择\n",
    "4. 模型评估\n",
    "5. 模型训练和测试\n",
    "6. 模型性能评估和优化\n",
    "7. 模型使用"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 环境搭建\n",
    "\n",
    "常用的几个选择方案:\n",
    "1. anaconda\n",
    "2. winpython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.Ipython使用\n",
    "\n",
    "Ipython是一个交互式的环境.  \n",
    "常用的一些快捷键:\n",
    "- ctrl+A:移动光标到头\n",
    "- ctrl+E:移动光标到尾部\n",
    "- ctrl+U:删除所在位置之前的代码\n",
    "- ctrl+K:删除光标后的代码\n",
    "- ctrl+l:清屏\n",
    "- ctrl+P:匹配用\n",
    "\n",
    "其他一些使用技巧:  \n",
    "- np.random.? 使用?来查阅文档\n",
    "- np.random.?? 使用??查看源码\n",
    "\n",
    "\n",
    "魔术命令:  \n",
    "- %run demo.py 运行python文件\n",
    "- %timeit np.dot(a,a), 快速评估代码的效率\n",
    "- %who %whos 查看当前环境下的变量列表\n",
    "- ! ls 使用! 可以配合使用一些系统命令\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "x=np.linspace(0,2*np.pi,num=100)\n",
    "y=np.sin(x)\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
